The present invention relates to video image sensors, and more particularly to colour and light sensing in relation to such sensors.
Red, green and blue (RGB) are three primary additive colours whereby individual components are added together to form a desired colour and this format is the basic colour space model which is widely used throughout imaging. In particular, broadcast, video and imaging standards make use of RGB signals to derive luminance and colour difference video signals, such as YUV, YIQ or YCbCr colour spaces. Colour spaces are mathematical representations of a set of colours. In YUV, Y represents the black and white information in the video signal (i.e. luminance), and U and V represent the colour information in the video signal (i.e. chrominance). The basic equations for converting between RGB and YUV are:Y=0.299R+0.587G+0.114B U=−0.147R−0.289G+0.436B U=0.492(B−Y) V=0.615R−0.515G−0.100B V=0.877(R−Y) 
In YIQ colour space, I stands for in-phase and Q stands for quadrature, which is the modulation method used to transmit the colour information; YIQ can be derived from YUV. YCbCr is a scaled and offset version of the YUV colour space.
Currently, in known colour video sensing devices such as video cameras, black and white pixel sensors are adapted to read colour information by disposing colour filters over the sensors, which sensors typically are CCD or CMOS sensors. A standard arrangement is for the pixels to be grouped in 2×2 blocks, with diagonally opposite pixels being responsive to green light and the other two diagonally opposite pixels being responsive to blue and red light respectively. These are known as RGB filters. The reason why there are two green pixels is that more image information is present in green light.
It has been noted that a problem of using such an arrangement of RGB filters is that it introduces a light attenuation of approximately 50:1. The share of the pixels between green, blue and red filters means that there is only 25% of the black and white pixel sensors for each of the blue and red filters, and 50% for the green filters. The result is that the sensor loses a great deal of colour resolution. Attempts are made in the downstream processing of the sensor output to recover the original resolution and a common technique is interpolation of the sensor output information using complex proprietary algorithms. This in essence involves estimating by interpolation what the colour response might be at a given pixel location based on the sensor outputs from the pixels surrounding that given pixel. However, since interpolation is a very approximate calculation its effectiveness varies widely depending on the complexity of the algorithm.
In addition, use of RGB filters leads to a reduction in sensitivity. Furthermore, there is also a reduction in the spatial resolution of the sensor, RGB colour filtering reducing resolution approximately by a factor of four.
A solution to the problem of loss of sensitivity due to RGB filters is to increase the length of time that the sensors are exposed to the scene to be captured on video. However, the knock-on effect of this is that the camera is more susceptible to shaking, and also blurring due to subject movement is more likely to occur. This is a particularly marked problem for CMOS sensors which are of much lower sensitivity than CCD sensors. Also, since sensor noise is additive, longer exposure periods results in higher noise floor, and thus the image signal is swamped.
As mentioned previously, in general, resolution of the image is determined by interpolation. Since there are a greater number of pixels in the green colour band and since also green light contains greater image information, this is used to increase the effective resolution of red and blue sensors. The main problem with this technique is that it is highly dependent on the quality of the algorithm used and its complexity. Sometimes the quality of the red and blue information can be improved further using green information.